Research

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This study evaluates the robustness of leading segmentation models against advanced adversarial attacks in the context of polyp and skin lesion identification.

Key Findings

• We benchmark the robustness of leading segmentation models under strong adversarial attacks, systematically identifying their failure modes in the context of polyp and skin lesion tasks.
• The first systematic study of bi-level nested architectures under adversarial threat, clarifying how multiscale fusion and cross-level aggregation influence resilience.
• Evidence that global feature representations mitigate adversarial perturbations, with distilled design guidelines for more robust medical segmentation systems.

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To mitigate feature redundancy in deep networks, we integrate the Efficient Channel Attention (ECA) module with the PVT encoder and augment it with an Uncertainty Augmented Context Attention (UACA) mechanism.

Key Findings

• PVT with Uncertainty-Augmented Context Attention. We integrate the PVT encoder with an uncertainty-augmented context attention mechanism, enabling the model to focus explicitly on ambiguous boundary regions. This mechanism improves the accuracy of boundary delineation by using uncertainty as a guiding signal.
• Integrating ECA with PVT Based Encoder. We incorporate ECA after each PVT stage, which refines feature representations while maintaining the original dimensionality. This enhancement improves the model’s discriminative power without introducing additional computational overhead.
• Explainability for Clinical Reliability. To ensure transparency and clinical applicability, we apply explainable AI (XAI) techniques, including uncertainty maps and heatmaps for foreground and edge regions. These methods improve interpretability and provide clinicians with more reliable decision making tools by highlighting areas of uncertainty and focus during polyp segmentation.

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In this study, we systematically investigate how established supervised segmentation models perform within three representative semi-supervised frameworks: Min-Max Similarity, Duo-SegNet, and Deep Co-Training.

Key Findings

• We represent a comprehensive evaluation of multiple supervised segmentation architectures in semi-supervised learning scenarios.
• We identify model characteristics that enable effective utilization of unlabeled data within semi-supervised frameworks.
• We provide empirical insights that inform the design of more better and dependable semi-supervised polyp segmentation methods, thereby reducing annotation requirements while maintaining high segmentation accuracy.

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Objective

This study evaluates the robustness of leading segmentation models against advanced adversarial attacks in the context of polyp and skin lesion identification.

Key Findings

• We benchmark the robustness of leading segmentation models under strong adversarial attacks, systematically identifying their failure modes in the context of polyp and skin lesion tasks.
• The first systematic study of bi-level nested architectures under adversarial threat, clarifying how multiscale fusion and cross-level aggregation influence resilience.
• Evidence that global feature representations mitigate adversarial perturbations, with distilled design guidelines for more robust medical segmentation systems.

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To develop SE-U2Net, a robust and efficient deep learning framework for road extraction from very high-resolution (VHR) satellite imagery by integrating the U²-Net architecture with Squeeze-and-Excitation (SE) modules, enabling selective channel refinement and multi-scale contextual feature learning without reliance on pre-trained backbones.

Key Findings

• Architectural Innovation: Integration of Squeeze-and-Excitation (SE) blocks for dynamic channel feature recalibration.
• Performance Improvement: Enhanced discrimination of minor roads through learned channel interdependencies.
• Computational Efficiency: Parameter-efficient attention mechanism achieved without complex spatial operations.

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To develop DoubleUNet++, an enhanced road extraction model that improves the contextual understanding and spatial precision of remote sensing image segmentation by integrating a Channel-Aware Gated Attention (CAGA) mechanism and Squeeze-and-Excitation (SE) layers into the DoubleUNet architecture.

Key Findings

• We propose Channel-Aware Gated Attention (CAGA) mechanism which has been integrated into skip connections of the secondary U-Net, enabling dynamic spatial feature weighting to emphasize critical road regions while suppressing irrelevant details
• A squeeze-and-excitation (SE) layer preceding the final activation in the attention pathway, adaptively recalibrating channel-wise feature responses to enhance discriminative power

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To systematically evaluate the performance and scalability of leading GNN-based spatial transcriptomics (ST) models—STAGATE, GraphST, and SEDR—on high-density next-generation datasets such as Stereo-seq and Slide-seq, and to identify their limitations in modeling complex spatial structures.

Key Findings

• Performance Evaluation: Comprehensive assessment using ARI, AMI, NMI, and HOMO metrics revealed modest and variable clustering performance across high-density datasets.
• - SEDR achieved the highest scores on Slide-seq datasets, but overall accuracy remained low.
• - Stereo-seq results were inconsistent, with no single model consistently outperforming others.
• Scalability Limitation: Existing GNN-based ST models struggle to generalize to high-resolution data, indicating poor scalability to large, complex tissue structures.
• Benchmark Establishment: This study provides a performance baseline for evaluating future models on next-generation ST platforms.
• Research Implication: Highlights the urgent need for developing new computational frameworks that are more robust, adaptive, and scalable for analyzing dense spatial transcriptomics data.

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To overcome the limitation of fixed spatial neighborhood scales in existing spatial transcriptomics (ST) models by developing En-STAGATE, an ensemble framework that integrates multiple spatial graphs across varying neighborhood radii to capture both local and global tissue structures for improved domain identification.

Key Findings

• Multi-Scale Ensemble Design: En-STAGATE constructs and fuses representations from multiple spatial graphs at different neighborhood radii, effectively modeling both fine-grained cellular interactions and large-scale tissue organization.
• Enhanced Biological Interpretability: The multi-scale integration provides a more comprehensive and biologically meaningful latent representation of tissues.
• Empirical Validation: Achieved competitive or superior performance across 14 benchmark datasets, outperforming STAGATE and other leading methods in several key cases.
• Improved Domain Delineation: Notably higher Adjusted Rand Index (ARI) scores on datasets such as 151674, 151675, 151676, and Mouse, demonstrating robustness in complex tissue structures.
• Key Insight: Highlights that a single neighborhood scale is insufficient for spatial domain identification, establishing multi-scale analysis as a critical paradigm in spatial transcriptomics.

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To address the over-smoothing and spatial bias in current spatial transcriptomics (ST) models by developing GraphST++, a multi-modal framework that integrates spatial, gene expression, and histological features for constructing biologically faithful tissue graphs.

Key Findings

• Multi-Modal Graph Construction: GraphST++ defines inter-spot relationships using a composite score combining spatial distance, gene expression similarity, and histological morphology, reducing over-reliance on proximity-based connections.
• Improved Tissue Representation: The proposed approach captures complex biological interactions and produces a more accurate reflection of the tissue microenvironment.
• Superior Performance: Demonstrated higher Adjusted Rand Index (ARI) compared to leading models, including GraphST, across multiple benchmark datasets such as human breast cancer (BRCA) and human brain tissues.
• Robustness Across Datasets: While not universally dominant, GraphST++ consistently benefits from morphological integration, yielding more stable and biologically interpretable clustering results.
• Broader Implications: Highlights the importance of multi-modal fusion in advancing spatial transcriptomics analysis and enabling more precise tissue domain identification.

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To overcome the limitation of existing graph-based spatial transcriptomics (ST) models in capturing long-range spatial dependencies by developing STAGATE++, a hybrid GAT–Transformer architecture that jointly learns local and global tissue representations.

Key Findings

• Hybrid Architecture Advantage: STAGATE++ integrates a Graph Attention Network (GAT) for precise local feature extraction with a Transformer encoder for modeling long-range spatial dependencies.
• - Enhanced Spatial Awareness: The model constructs a holistic and spatially coherent tissue representation, improving understanding of tissue organization.
• Empirical Superiority: Demonstrated state-of-the-art performance across 14 benchmark datasets, consistently outperforming existing methods in spatial domain identification.
• Validation Metric: Significant improvements in Adjusted Rand Index (ARI) confirm the importance of global context modeling in tissue delineation.
• Future Extensions: Plans include incorporating histopathological image features, applying self-supervised training for better generalization, and integrating GNN interpretability techniques for biological insight and clinical transparency.

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